Maswali ya Kutumikia Skew ya Kipengee cha Mtandaoni na Nje ya Mtandao

Majibu na maelezo huonekana unapojibu maswali.

1. Mafunzo/kuhudumia skew ni nini?
   • Wakati mtindo umefunzwa kwenye GPU lakini unatumiwa kwenye CPU
   • Wakati muundo ni mkubwa sana kutoshea kwenye kumbukumbu
   • Wakati vipengele vilivyokokotwa nje ya mtandao wakati wa mafunzo vinatofautiana na vile vinavyotolewa mtandaoni katika uzalishaji
   • Wakati mafunzo huchukua muda mrefu kuliko ilivyotarajiwa
2. When building a historical training row, which feature timestamps should a point-in-time join allow?
   • Values available at or before that row’s prediction time
   • Values recorded only after the outcome
   • The latest values regardless of historical time
   • Only values from the next calendar month
3. Ni kipi kati ya zifuatazo ambacho ni sababu ya kawaida ya usahihi wa kuongezeka kwa nje ya mtandao ambao huporomoka katika toleo la umma?
   • Kwa kutumia mifano michache sana ya mafunzo
   • Mafunzo kwenye mashine yenye kasi zaidi kuliko kuhudumia
   • Kuhifadhi vipengele katika hifadhidata
   • Uvujaji wa data, ambapo mafunzo hutumia taarifa zisizopatikana kwa wakati wa utabiri
4. A logged live feature distribution differs from training. What should the feature-serving team infer first?
   • The serving code is certainly defective
   • There is a mismatch to investigate; it may be drift or serving skew
   • The feature has no predictive value
   • The model must be running on different hardware
5. Kwa nini kipengele cha mtandaoni kilichoakibishwa kama 'maagizo katika saa iliyopita' ni hatari ya kupotosha?
   • Thamani iliyoakibishwa inaweza kwenda nyuma na isilingane tena na thamani mpya iliyokokotwa inayotumika katika mafunzo
   • Uakibishaji daima huboresha usahihi wa muundo
   • Kuhifadhi akiba hufanya kipengele kisiweze kukokotoa nje ya mtandao
   • Vipengele vilivyohifadhiwa haviwezi kuwa nambari
6. Why share one feature definition across batch training and online serving?
   • To guarantee every cached value is fresh
   • To eliminate changes in incoming populations
   • To make timestamp checks unnecessary
   • To reduce differences caused by separate transformation implementations
7. A transaction model uses a chargeback flag recorded days after purchase. Why is its offline feature join invalid?
   • The flag is categorical rather than numerical
   • The flag was unavailable when the purchase prediction was made
   • Chargeback flags cannot be stored historically
   • Only GPU models may use transaction features
8. Training recomputes a one-hour order count, but serving reads a ten-minute-old cached count. Which check targets this skew?
   • Increase the number of model parameters
   • Remove all timestamps from the feature store
   • Compare counts for matching entities and times while recording cache freshness
   • Switch both paths to a different programming language